物理层通信网络安全:一种DRL和相位优化方法

IF 1.4 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Mian Muhammad Kamal, Syed Zain Ul Abideen, Amin Sharafian, Anwar Hassan Ibrahim, Muhammad Islam, Shabana Habib
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引用次数: 0

摘要

随着物联网(iot)的兴起,在潜在窃听者过多的情况下,如何有效地保护多个用户之间的通信已成为一个重要问题。本文扩展了早期的研究,从单用户和单窃听者场景转移到复杂的多用户和多窃听者环境,并首次结合了先进的物理层安全(PLS)技术。使用可重构智能表面(RISs)增强了预期用户的信号强度和质量,而对非预期用户的信号则被抑制。RIS相移的实时控制是通过深度确定性策略梯度(DDPG)算法实现的,这种控制显著地改变了安全性和能源浪费之间的权衡。仿真结果表明,该方法可以在人口密集的城市中心扩展,同时提高不同无线移动信道的误码率(BER)性能和整体能源效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Securing Communication Networks at the Physical Layer: A DRL and Phase Optimization Approach

Securing Communication Networks at the Physical Layer: A DRL and Phase Optimization Approach

Securing Communication Networks at the Physical Layer: A DRL and Phase Optimization Approach

Securing Communication Networks at the Physical Layer: A DRL and Phase Optimization Approach

Securing Communication Networks at the Physical Layer: A DRL and Phase Optimization Approach

Securing communication between multiple users efficiently while there are too many potential eavesdroppers has become an important issue with the rise of the Internet of Things (IoTs). This paper extends on earlier research, moving from a single-user and single-eavesdropper scenario to a complex multiuser and multieavesdropper context, and incorporates an advanced physical layer security (PLS) technique for the first time. Using reconfigurable intelligent surfaces (RISs) enhances the strength and quality of signals for intended users, while those to the unintended users are suppressed. Real-time control of the RIS phase shifts is enabled through a deep deterministic policy gradient (DDPG) algorithm and this control significantly changes the trade-off between security and energy wastage. The simulation results demonstrate that the developed approach can scale up in densely populated urban centers, while increasing the bit error rate (BER) performance and the overall energy efficiency across different wireless mobile channels.

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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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